Overview

Dataset statistics

Number of variables32
Number of observations8124
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 MiB
Average record size in memory256.0 B

Variable types

Numeric17
Categorical13
Boolean2

Alerts

post_code has a high cardinality: 8050 distinct valuesHigh cardinality
post_area has a high cardinality: 1937 distinct valuesHigh cardinality
year_last_moved is highly overall correlated with age_band and 2 other fieldsHigh correlation
Average_Credit_Card_Transaction is highly overall correlated with Investment_in_CommudityHigh correlation
Balance_Transfer is highly overall correlated with Life_Insurance and 2 other fieldsHigh correlation
Term_Deposit is highly overall correlated with Investment_in_CommudityHigh correlation
Life_Insurance is highly overall correlated with Balance_Transfer and 5 other fieldsHigh correlation
Medical_Insurance is highly overall correlated with Investment_in_CommudityHigh correlation
Average_A/C_Balance is highly overall correlated with Life_Insurance and 3 other fieldsHigh correlation
Personal_Loan is highly overall correlated with Investment_in_EquityHigh correlation
Investment_in_Mutual_Fund is highly overall correlated with Investment_in_Equity and 2 other fieldsHigh correlation
Online_Purchase_Amount is highly overall correlated with Investment_in_EquityHigh correlation
Investment_in_Commudity is highly overall correlated with Average_Credit_Card_Transaction and 7 other fieldsHigh correlation
Investment_in_Equity is highly overall correlated with Life_Insurance and 7 other fieldsHigh correlation
Investment_in_Derivative is highly overall correlated with Life_Insurance and 5 other fieldsHigh correlation
Portfolio_Balance is highly overall correlated with Balance_Transfer and 6 other fieldsHigh correlation
age_band is highly overall correlated with year_last_movedHigh correlation
status is highly overall correlated with year_last_movedHigh correlation
home_status is highly overall correlated with year_last_movedHigh correlation
TVarea is highly overall correlated with regionHigh correlation
region is highly overall correlated with TVareaHigh correlation
home_status is highly imbalanced (77.6%)Imbalance
self_employed is highly imbalanced (62.8%)Imbalance
Revenue_Grid is highly imbalanced (51.3%)Imbalance
Personal_Loan is highly skewed (γ1 = 26.15959592)Skewed
Online_Purchase_Amount is highly skewed (γ1 = 21.76395425)Skewed
REF_NO is uniformly distributedUniform
post_code is uniformly distributedUniform
REF_NO has unique valuesUnique
Average_Credit_Card_Transaction has 4989 (61.4%) zerosZeros
Balance_Transfer has 3524 (43.4%) zerosZeros
Term_Deposit has 4587 (56.5%) zerosZeros
Life_Insurance has 2454 (30.2%) zerosZeros
Medical_Insurance has 4046 (49.8%) zerosZeros
Average_A/C_Balance has 2806 (34.5%) zerosZeros
Personal_Loan has 5134 (63.2%) zerosZeros
Investment_in_Mutual_Fund has 2602 (32.0%) zerosZeros
Investment_Tax_Saving_Bond has 5133 (63.2%) zerosZeros
Home_Loan has 5609 (69.0%) zerosZeros
Online_Purchase_Amount has 5700 (70.2%) zerosZeros
Investment_in_Commudity has 825 (10.2%) zerosZeros
Investment_in_Equity has 915 (11.3%) zerosZeros
Investment_in_Derivative has 445 (5.5%) zerosZeros

Reproduction

Analysis started2023-06-05 22:31:50.436133
Analysis finished2023-06-05 22:32:21.812799
Duration31.38 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

REF_NO
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct8124
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5797.3433
Minimum2
Maximum11518
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2023-06-05T22:32:21.874057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile592.15
Q12924.75
median5811.5
Q38681.5
95-th percentile10947.85
Maximum11518
Range11516
Interquartile range (IQR)5756.75

Descriptive statistics

Standard deviation3322.4976
Coefficient of variation (CV)0.57310692
Kurtosis-1.2005268
Mean5797.3433
Median Absolute Deviation (MAD)2878.5
Skewness-0.012802837
Sum47097617
Variance11038990
MonotonicityNot monotonic
2023-06-05T22:32:21.990895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5466 1
 
< 0.1%
4702 1
 
< 0.1%
2082 1
 
< 0.1%
1122 1
 
< 0.1%
10963 1
 
< 0.1%
3815 1
 
< 0.1%
7162 1
 
< 0.1%
4361 1
 
< 0.1%
10304 1
 
< 0.1%
11484 1
 
< 0.1%
Other values (8114) 8114
99.9%
ValueCountFrequency (%)
2 1
< 0.1%
3 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
11 1
< 0.1%
14 1
< 0.1%
16 1
< 0.1%
18 1
< 0.1%
ValueCountFrequency (%)
11518 1
< 0.1%
11514 1
< 0.1%
11513 1
< 0.1%
11512 1
< 0.1%
11511 1
< 0.1%
11507 1
< 0.1%
11506 1
< 0.1%
11503 1
< 0.1%
11502 1
< 0.1%
11501 1
< 0.1%

children
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
Zero
4991 
1
1474 
2
1271 
3
 
375
4+
 
13

Length

Max length4
Median length4
Mean length2.8446578
Min length1

Characters and Unicode

Total characters23110
Distinct characters9
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd rowZero
3rd row1
4th row2
5th rowZero

Common Values

ValueCountFrequency (%)
Zero 4991
61.4%
1 1474
 
18.1%
2 1271
 
15.6%
3 375
 
4.6%
4+ 13
 
0.2%

Length

2023-06-05T22:32:22.093209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T22:32:22.199223image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
zero 4991
61.4%
1 1474
 
18.1%
2 1271
 
15.6%
3 375
 
4.6%
4 13
 
0.2%

Most occurring characters

ValueCountFrequency (%)
Z 4991
21.6%
e 4991
21.6%
r 4991
21.6%
o 4991
21.6%
1 1474
 
6.4%
2 1271
 
5.5%
3 375
 
1.6%
4 13
 
0.1%
+ 13
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14973
64.8%
Uppercase Letter 4991
 
21.6%
Decimal Number 3133
 
13.6%
Math Symbol 13
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1474
47.0%
2 1271
40.6%
3 375
 
12.0%
4 13
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
e 4991
33.3%
r 4991
33.3%
o 4991
33.3%
Uppercase Letter
ValueCountFrequency (%)
Z 4991
100.0%
Math Symbol
ValueCountFrequency (%)
+ 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19964
86.4%
Common 3146
 
13.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1474
46.9%
2 1271
40.4%
3 375
 
11.9%
4 13
 
0.4%
+ 13
 
0.4%
Latin
ValueCountFrequency (%)
Z 4991
25.0%
e 4991
25.0%
r 4991
25.0%
o 4991
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Z 4991
21.6%
e 4991
21.6%
r 4991
21.6%
o 4991
21.6%
1 1474
 
6.4%
2 1271
 
5.5%
3 375
 
1.6%
4 13
 
0.1%
+ 13
 
0.1%

age_band
Categorical

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
45-50
1098 
41-45
903 
36-40
895 
55-60
865 
31-35
840 
Other values (8)
3523 

Length

Max length7
Median length5
Mean length4.927868
Min length3

Characters and Unicode

Total characters40034
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row31-35
2nd row45-50
3rd row36-40
4th row31-35
5th row55-60

Common Values

ValueCountFrequency (%)
45-50 1098
13.5%
41-45 903
11.1%
36-40 895
11.0%
55-60 865
10.6%
31-35 840
10.3%
51-55 833
10.3%
26-30 735
9.0%
61-65 700
8.6%
65-70 468
5.8%
22-25 356
 
4.4%
Other values (3) 431
 
5.3%

Length

2023-06-05T22:32:22.292775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
45-50 1098
13.5%
41-45 903
11.1%
36-40 895
11.0%
55-60 865
10.6%
31-35 840
10.3%
51-55 833
10.3%
26-30 735
9.0%
61-65 700
8.6%
65-70 468
5.8%
22-25 356
 
4.4%
Other values (3) 431
 
5.3%

Most occurring characters

ValueCountFrequency (%)
5 9692
24.2%
- 7743
19.3%
6 4363
10.9%
0 4061
10.1%
4 3799
 
9.5%
1 3713
 
9.3%
3 3310
 
8.3%
2 1853
 
4.6%
7 805
 
2.0%
+ 337
 
0.8%
Other values (6) 358
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31646
79.0%
Dash Punctuation 7743
 
19.3%
Math Symbol 337
 
0.8%
Lowercase Letter 264
 
0.7%
Uppercase Letter 44
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 9692
30.6%
6 4363
13.8%
0 4061
12.8%
4 3799
 
12.0%
1 3713
 
11.7%
3 3310
 
10.5%
2 1853
 
5.9%
7 805
 
2.5%
8 50
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
n 132
50.0%
k 44
 
16.7%
o 44
 
16.7%
w 44
 
16.7%
Dash Punctuation
ValueCountFrequency (%)
- 7743
100.0%
Math Symbol
ValueCountFrequency (%)
+ 337
100.0%
Uppercase Letter
ValueCountFrequency (%)
U 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39726
99.2%
Latin 308
 
0.8%

Most frequent character per script

Common
ValueCountFrequency (%)
5 9692
24.4%
- 7743
19.5%
6 4363
11.0%
0 4061
10.2%
4 3799
 
9.6%
1 3713
 
9.3%
3 3310
 
8.3%
2 1853
 
4.7%
7 805
 
2.0%
+ 337
 
0.8%
Latin
ValueCountFrequency (%)
n 132
42.9%
U 44
 
14.3%
k 44
 
14.3%
o 44
 
14.3%
w 44
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40034
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 9692
24.2%
- 7743
19.3%
6 4363
10.9%
0 4061
10.1%
4 3799
 
9.5%
1 3713
 
9.3%
3 3310
 
8.3%
2 1853
 
4.6%
7 805
 
2.0%
+ 337
 
0.8%
Other values (6) 358
 
0.9%

status
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
Partner
6124 
Single/Never Married
881 
Divorced/Separated
 
569
Widowed
 
510
Unknown
 
40

Length

Max length20
Median length7
Mean length9.1802068
Min length7

Characters and Unicode

Total characters74580
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPartner
2nd rowPartner
3rd rowPartner
4th rowPartner
5th rowPartner

Common Values

ValueCountFrequency (%)
Partner 6124
75.4%
Single/Never Married 881
 
10.8%
Divorced/Separated 569
 
7.0%
Widowed 510
 
6.3%
Unknown 40
 
0.5%

Length

2023-06-05T22:32:22.389677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T22:32:22.496775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
partner 6124
68.0%
single/never 881
 
9.8%
married 881
 
9.8%
divorced/separated 569
 
6.3%
widowed 510
 
5.7%
unknown 40
 
0.4%

Most occurring characters

ValueCountFrequency (%)
r 16029
21.5%
e 11865
15.9%
a 8143
10.9%
n 7125
9.6%
t 6693
9.0%
P 6124
 
8.2%
d 3039
 
4.1%
i 2841
 
3.8%
/ 1450
 
1.9%
v 1450
 
1.9%
Other values (14) 9821
13.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 61794
82.9%
Uppercase Letter 10455
 
14.0%
Other Punctuation 1450
 
1.9%
Space Separator 881
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 16029
25.9%
e 11865
19.2%
a 8143
13.2%
n 7125
11.5%
t 6693
10.8%
d 3039
 
4.9%
i 2841
 
4.6%
v 1450
 
2.3%
o 1119
 
1.8%
l 881
 
1.4%
Other values (5) 2609
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
P 6124
58.6%
S 1450
 
13.9%
N 881
 
8.4%
M 881
 
8.4%
D 569
 
5.4%
W 510
 
4.9%
U 40
 
0.4%
Other Punctuation
ValueCountFrequency (%)
/ 1450
100.0%
Space Separator
ValueCountFrequency (%)
881
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 72249
96.9%
Common 2331
 
3.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 16029
22.2%
e 11865
16.4%
a 8143
11.3%
n 7125
9.9%
t 6693
9.3%
P 6124
 
8.5%
d 3039
 
4.2%
i 2841
 
3.9%
v 1450
 
2.0%
S 1450
 
2.0%
Other values (12) 7490
10.4%
Common
ValueCountFrequency (%)
/ 1450
62.2%
881
37.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74580
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 16029
21.5%
e 11865
15.9%
a 8143
10.9%
n 7125
9.6%
t 6693
9.0%
P 6124
 
8.2%
d 3039
 
4.1%
i 2841
 
3.8%
/ 1450
 
1.9%
v 1450
 
1.9%
Other values (14) 9821
13.2%

occupation
Categorical

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
Professional
1949 
Retired
1799 
Secretarial/Admin
1435 
Housewife
984 
Business Manager
578 
Other values (4)
1379 

Length

Max length17
Median length16
Mean length11.075209
Min length5

Characters and Unicode

Total characters89975
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowProfessional
2nd rowSecretarial/Admin
3rd rowManual Worker
4th rowManual Worker
5th rowHousewife

Common Values

ValueCountFrequency (%)
Professional 1949
24.0%
Retired 1799
22.1%
Secretarial/Admin 1435
17.7%
Housewife 984
12.1%
Business Manager 578
 
7.1%
Manual Worker 451
 
5.6%
Unknown 449
 
5.5%
Other 432
 
5.3%
Student 47
 
0.6%

Length

2023-06-05T22:32:22.589202image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T22:32:22.704451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
professional 1949
21.3%
retired 1799
19.7%
secretarial/admin 1435
15.7%
housewife 984
10.8%
business 578
 
6.3%
manager 578
 
6.3%
manual 451
 
4.9%
worker 451
 
4.9%
unknown 449
 
4.9%
other 432
 
4.7%

Most occurring characters

ValueCountFrequency (%)
e 12471
13.9%
r 8530
 
9.5%
i 8180
 
9.1%
a 6877
 
7.6%
s 6616
 
7.4%
n 6385
 
7.1%
o 5782
 
6.4%
l 3835
 
4.3%
t 3760
 
4.2%
d 3281
 
3.6%
Other values (20) 24258
27.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 76923
85.5%
Uppercase Letter 10588
 
11.8%
Other Punctuation 1435
 
1.6%
Space Separator 1029
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 12471
16.2%
r 8530
11.1%
i 8180
10.6%
a 6877
8.9%
s 6616
8.6%
n 6385
8.3%
o 5782
7.5%
l 3835
 
5.0%
t 3760
 
4.9%
d 3281
 
4.3%
Other values (8) 11206
14.6%
Uppercase Letter
ValueCountFrequency (%)
P 1949
18.4%
R 1799
17.0%
S 1482
14.0%
A 1435
13.6%
M 1029
9.7%
H 984
9.3%
B 578
 
5.5%
W 451
 
4.3%
U 449
 
4.2%
O 432
 
4.1%
Other Punctuation
ValueCountFrequency (%)
/ 1435
100.0%
Space Separator
ValueCountFrequency (%)
1029
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 87511
97.3%
Common 2464
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 12471
14.3%
r 8530
 
9.7%
i 8180
 
9.3%
a 6877
 
7.9%
s 6616
 
7.6%
n 6385
 
7.3%
o 5782
 
6.6%
l 3835
 
4.4%
t 3760
 
4.3%
d 3281
 
3.7%
Other values (18) 21794
24.9%
Common
ValueCountFrequency (%)
/ 1435
58.2%
1029
41.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89975
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 12471
13.9%
r 8530
 
9.5%
i 8180
 
9.1%
a 6877
 
7.6%
s 6616
 
7.4%
n 6385
 
7.1%
o 5782
 
6.4%
l 3835
 
4.3%
t 3760
 
4.2%
d 3281
 
3.6%
Other values (20) 24258
27.0%
Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
Unknown
1942 
Professional
1620 
Retired
1558 
Manual Worker
1222 
Business Manager
575 
Other values (4)
1207 

Length

Max length17
Median length16
Mean length10.203964
Min length5

Characters and Unicode

Total characters82897
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowProfessional
2nd rowProfessional
3rd rowManual Worker
4th rowManual Worker
5th rowProfessional

Common Values

ValueCountFrequency (%)
Unknown 1942
23.9%
Professional 1620
19.9%
Retired 1558
19.2%
Manual Worker 1222
15.0%
Business Manager 575
 
7.1%
Secretarial/Admin 510
 
6.3%
Housewife 422
 
5.2%
Other 261
 
3.2%
Student 14
 
0.2%

Length

2023-06-05T22:32:22.816844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T22:32:22.934494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
unknown 1942
19.6%
professional 1620
16.3%
retired 1558
15.7%
manual 1222
12.3%
worker 1222
12.3%
business 575
 
5.8%
manager 575
 
5.8%
secretarial/admin 510
 
5.1%
housewife 422
 
4.3%
other 261
 
2.6%

Most occurring characters

ValueCountFrequency (%)
n 10342
12.5%
e 9247
 
11.2%
r 7478
 
9.0%
o 6826
 
8.2%
a 6234
 
7.5%
s 5387
 
6.5%
i 5195
 
6.3%
l 3352
 
4.0%
k 3164
 
3.8%
w 2364
 
2.9%
Other values (20) 23308
28.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 70159
84.6%
Uppercase Letter 10431
 
12.6%
Space Separator 1797
 
2.2%
Other Punctuation 510
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 10342
14.7%
e 9247
13.2%
r 7478
10.7%
o 6826
9.7%
a 6234
8.9%
s 5387
7.7%
i 5195
7.4%
l 3352
 
4.8%
k 3164
 
4.5%
w 2364
 
3.4%
Other values (8) 10570
15.1%
Uppercase Letter
ValueCountFrequency (%)
U 1942
18.6%
M 1797
17.2%
P 1620
15.5%
R 1558
14.9%
W 1222
11.7%
B 575
 
5.5%
S 524
 
5.0%
A 510
 
4.9%
H 422
 
4.0%
O 261
 
2.5%
Space Separator
ValueCountFrequency (%)
1797
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 510
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 80590
97.2%
Common 2307
 
2.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 10342
12.8%
e 9247
11.5%
r 7478
 
9.3%
o 6826
 
8.5%
a 6234
 
7.7%
s 5387
 
6.7%
i 5195
 
6.4%
l 3352
 
4.2%
k 3164
 
3.9%
w 2364
 
2.9%
Other values (18) 21001
26.1%
Common
ValueCountFrequency (%)
1797
77.9%
/ 510
 
22.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 82897
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 10342
12.5%
e 9247
 
11.2%
r 7478
 
9.0%
o 6826
 
8.2%
a 6234
 
7.5%
s 5387
 
6.5%
i 5195
 
6.3%
l 3352
 
4.0%
k 3164
 
3.8%
w 2364
 
2.9%
Other values (20) 23308
28.1%

home_status
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
Own Home
7506 
Rent from Council/HA
 
279
Rent Privately
 
205
Live in Parental Hom
 
90
Unclassified
 
44

Length

Max length20
Median length8
Mean length8.7181192
Min length8

Characters and Unicode

Total characters70826
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOwn Home
2nd rowOwn Home
3rd rowRent Privately
4th rowOwn Home
5th rowOwn Home

Common Values

ValueCountFrequency (%)
Own Home 7506
92.4%
Rent from Council/HA 279
 
3.4%
Rent Privately 205
 
2.5%
Live in Parental Hom 90
 
1.1%
Unclassified 44
 
0.5%

Length

2023-06-05T22:32:23.047029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T22:32:23.154561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
own 7506
45.0%
home 7506
45.0%
rent 484
 
2.9%
from 279
 
1.7%
council/ha 279
 
1.7%
privately 205
 
1.2%
live 90
 
0.5%
in 90
 
0.5%
parental 90
 
0.5%
hom 90
 
0.5%

Most occurring characters

ValueCountFrequency (%)
8539
12.1%
n 8493
12.0%
e 8419
11.9%
o 8154
11.5%
H 7875
11.1%
m 7875
11.1%
O 7506
10.6%
w 7506
10.6%
t 779
 
1.1%
i 752
 
1.1%
Other values (17) 4928
7.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 45156
63.8%
Uppercase Letter 16852
 
23.8%
Space Separator 8539
 
12.1%
Other Punctuation 279
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 8493
18.8%
e 8419
18.6%
o 8154
18.1%
m 7875
17.4%
w 7506
16.6%
t 779
 
1.7%
i 752
 
1.7%
l 618
 
1.4%
r 574
 
1.3%
a 429
 
1.0%
Other values (7) 1557
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
H 7875
46.7%
O 7506
44.5%
R 484
 
2.9%
P 295
 
1.8%
A 279
 
1.7%
C 279
 
1.7%
L 90
 
0.5%
U 44
 
0.3%
Space Separator
ValueCountFrequency (%)
8539
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 279
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 62008
87.5%
Common 8818
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 8493
13.7%
e 8419
13.6%
o 8154
13.1%
H 7875
12.7%
m 7875
12.7%
O 7506
12.1%
w 7506
12.1%
t 779
 
1.3%
i 752
 
1.2%
l 618
 
1.0%
Other values (15) 4031
6.5%
Common
ValueCountFrequency (%)
8539
96.8%
/ 279
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70826
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8539
12.1%
n 8493
12.0%
e 8419
11.9%
o 8154
11.5%
H 7875
11.1%
m 7875
11.1%
O 7506
10.6%
w 7506
10.6%
t 779
 
1.1%
i 752
 
1.1%
Other values (17) 4928
7.0%

family_income
Categorical

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
>=35,000
2014 
<27,500, >=25,000
969 
<30,000, >=27,500
796 
<25,000, >=22,500
656 
<12,500, >=10,000
535 
Other values (8)
3154 

Length

Max length17
Median length17
Mean length14.349089
Min length7

Characters and Unicode

Total characters116572
Distinct characters18
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row>=35,000
2nd row>=35,000
3rd row<22,500, >=20,000
4th row<25,000, >=22,500
5th row>=35,000

Common Values

ValueCountFrequency (%)
>=35,000 2014
24.8%
<27,500, >=25,000 969
11.9%
<30,000, >=27,500 796
 
9.8%
<25,000, >=22,500 656
 
8.1%
<12,500, >=10,000 535
 
6.6%
<20,000, >=17,500 525
 
6.5%
<17,500, >=15,000 521
 
6.4%
<15,000, >=12,500 508
 
6.3%
<22,500, >=20,000 479
 
5.9%
<10,000, >= 8,000 452
 
5.6%
Other values (3) 669
 
8.2%

Length

2023-06-05T22:32:23.243057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
35,000 2014
13.2%
27,500 1765
11.6%
25,000 1625
10.7%
1341
8.8%
22,500 1135
7.5%
17,500 1046
6.9%
12,500 1043
6.8%
15,000 1029
6.8%
20,000 1004
6.6%
10,000 987
6.5%
Other values (4) 2245
14.7%

Most occurring characters

ValueCountFrequency (%)
0 39153
33.6%
, 19554
16.8%
5 9657
 
8.3%
> 7783
 
6.7%
= 7783
 
6.7%
2 7707
 
6.6%
7110
 
6.1%
< 6002
 
5.1%
1 4105
 
3.5%
7 2811
 
2.4%
Other values (8) 4907
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 67584
58.0%
Math Symbol 21568
 
18.5%
Other Punctuation 19554
 
16.8%
Space Separator 7110
 
6.1%
Lowercase Letter 648
 
0.6%
Uppercase Letter 108
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39153
57.9%
5 9657
 
14.3%
2 7707
 
11.4%
1 4105
 
6.1%
7 2811
 
4.2%
3 2810
 
4.2%
8 780
 
1.2%
4 561
 
0.8%
Lowercase Letter
ValueCountFrequency (%)
n 324
50.0%
k 108
 
16.7%
o 108
 
16.7%
w 108
 
16.7%
Math Symbol
ValueCountFrequency (%)
> 7783
36.1%
= 7783
36.1%
< 6002
27.8%
Other Punctuation
ValueCountFrequency (%)
, 19554
100.0%
Space Separator
ValueCountFrequency (%)
7110
100.0%
Uppercase Letter
ValueCountFrequency (%)
U 108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 115816
99.4%
Latin 756
 
0.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 39153
33.8%
, 19554
16.9%
5 9657
 
8.3%
> 7783
 
6.7%
= 7783
 
6.7%
2 7707
 
6.7%
7110
 
6.1%
< 6002
 
5.2%
1 4105
 
3.5%
7 2811
 
2.4%
Other values (3) 4151
 
3.6%
Latin
ValueCountFrequency (%)
n 324
42.9%
U 108
 
14.3%
k 108
 
14.3%
o 108
 
14.3%
w 108
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 116572
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39153
33.6%
, 19554
16.8%
5 9657
 
8.3%
> 7783
 
6.7%
= 7783
 
6.7%
2 7707
 
6.6%
7110
 
6.1%
< 6002
 
5.1%
1 4105
 
3.5%
7 2811
 
2.4%
Other values (8) 4907
 
4.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
False
7543 
True
 
581
ValueCountFrequency (%)
False 7543
92.8%
True 581
 
7.2%
2023-06-05T22:32:23.336500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
False
7207 
True
917 
ValueCountFrequency (%)
False 7207
88.7%
True 917
 
11.3%
2023-06-05T22:32:23.421425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

year_last_moved
Real number (ℝ)

Distinct94
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1967.8769
Minimum0
Maximum1999
Zeros69
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2023-06-05T22:32:23.516999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1961
Q11978
median1988
Q31994
95-th percentile1998
Maximum1999
Range1999
Interquartile range (IQR)16

Descriptive statistics

Standard deviation182.56379
Coefficient of variation (CV)0.092771954
Kurtosis111.75814
Mean1967.8769
Median Absolute Deviation (MAD)7
Skewness-10.639671
Sum15987032
Variance33329.536
MonotonicityNot monotonic
2023-06-05T22:32:23.632136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1997 548
 
6.7%
1996 525
 
6.5%
1998 434
 
5.3%
1994 423
 
5.2%
1995 386
 
4.8%
1988 338
 
4.2%
1993 323
 
4.0%
1986 316
 
3.9%
1992 294
 
3.6%
1987 284
 
3.5%
Other values (84) 4253
52.4%
ValueCountFrequency (%)
0 69
0.8%
1901 2
 
< 0.1%
1902 2
 
< 0.1%
1903 1
 
< 0.1%
1904 2
 
< 0.1%
1905 3
 
< 0.1%
1906 1
 
< 0.1%
1907 2
 
< 0.1%
1908 3
 
< 0.1%
1909 1
 
< 0.1%
ValueCountFrequency (%)
1999 51
 
0.6%
1998 434
5.3%
1997 548
6.7%
1996 525
6.5%
1995 386
4.8%
1994 423
5.2%
1993 323
4.0%
1992 294
3.6%
1991 256
3.2%
1990 273
3.4%

TVarea
Categorical

Distinct14
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
Central
1294 
Carlton
1237 
Meridian
977 
Yorkshire
847 
Granada
824 
Other values (9)
2945 

Length

Max length13
Median length11
Mean length7.4322994
Min length3

Characters and Unicode

Total characters60380
Distinct characters32
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMeridian
2nd rowMeridian
3rd rowHTV
4th rowScottish TV
5th rowYorkshire

Common Values

ValueCountFrequency (%)
Central 1294
15.9%
Carlton 1237
15.2%
Meridian 977
12.0%
Yorkshire 847
10.4%
Granada 824
10.1%
HTV 683
8.4%
Anglia 597
7.3%
Tyne Tees 433
 
5.3%
Scottish TV 406
 
5.0%
TV South West 286
 
3.5%
Other values (4) 540
6.6%

Length

2023-06-05T22:32:23.737440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
central 1294
13.6%
carlton 1237
13.0%
meridian 977
10.2%
yorkshire 847
8.9%
granada 824
8.6%
tv 692
7.3%
htv 683
7.2%
anglia 597
 
6.3%
tees 433
 
4.5%
tyne 433
 
4.5%
Other values (7) 1518
15.9%

Most occurring characters

ValueCountFrequency (%)
a 6927
11.5%
r 6488
 
10.7%
n 5999
 
9.9%
e 4914
 
8.1%
t 4050
 
6.7%
i 3979
 
6.6%
l 3263
 
5.4%
o 3006
 
5.0%
C 2531
 
4.2%
T 2241
 
3.7%
Other values (22) 16982
28.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 47376
78.5%
Uppercase Letter 11593
 
19.2%
Space Separator 1411
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6927
14.6%
r 6488
13.7%
n 5999
12.7%
e 4914
10.4%
t 4050
8.5%
i 3979
8.4%
l 3263
6.9%
o 3006
6.3%
s 2107
 
4.4%
d 1877
 
4.0%
Other values (9) 4766
10.1%
Uppercase Letter
ValueCountFrequency (%)
C 2531
21.8%
T 2241
19.3%
V 1375
11.9%
G 999
 
8.6%
M 977
 
8.4%
Y 847
 
7.3%
S 692
 
6.0%
H 683
 
5.9%
A 597
 
5.1%
U 289
 
2.5%
Other values (2) 362
 
3.1%
Space Separator
ValueCountFrequency (%)
1411
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 58969
97.7%
Common 1411
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6927
11.7%
r 6488
11.0%
n 5999
 
10.2%
e 4914
 
8.3%
t 4050
 
6.9%
i 3979
 
6.7%
l 3263
 
5.5%
o 3006
 
5.1%
C 2531
 
4.3%
T 2241
 
3.8%
Other values (21) 15571
26.4%
Common
ValueCountFrequency (%)
1411
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60380
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6927
11.5%
r 6488
 
10.7%
n 5999
 
9.9%
e 4914
 
8.1%
t 4050
 
6.7%
i 3979
 
6.6%
l 3263
 
5.4%
o 3006
 
5.0%
C 2531
 
4.2%
T 2241
 
3.7%
Other values (22) 16982
28.1%

post_code
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct8050
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
NP18 3TJ
 
2
S70 5JE
 
2
S65 3DL
 
2
CM6 2JA
 
2
G64 3PL
 
2
Other values (8045)
8114 

Length

Max length8
Median length7
Mean length7.4601182
Min length6

Characters and Unicode

Total characters60606
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7976 ?
Unique (%)98.2%

Sample

1st rowM51 0GU
2nd rowL40 2AG
3rd rowTA19 9PT
4th rowFK2 9NG
5th rowLS23 7DJ

Common Values

ValueCountFrequency (%)
NP18 3TJ 2
 
< 0.1%
S70 5JE 2
 
< 0.1%
S65 3DL 2
 
< 0.1%
CM6 2JA 2
 
< 0.1%
G64 3PL 2
 
< 0.1%
DT4 0LN 2
 
< 0.1%
B98 7NR 2
 
< 0.1%
L9 7BN 2
 
< 0.1%
DY8 5HN 2
 
< 0.1%
CF47 8PQ 2
 
< 0.1%
Other values (8040) 8104
99.8%

Length

2023-06-05T22:32:23.825832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pr5 28
 
0.2%
tq12 22
 
0.1%
wa4 22
 
0.1%
le7 21
 
0.1%
pr4 21
 
0.1%
pr8 21
 
0.1%
ts5 20
 
0.1%
m12 20
 
0.1%
m13 19
 
0.1%
m33 19
 
0.1%
Other values (4888) 16035
98.7%

Most occurring characters

ValueCountFrequency (%)
8124
 
13.4%
1 4089
 
6.7%
2 2848
 
4.7%
3 2429
 
4.0%
L 2385
 
3.9%
N 2142
 
3.5%
S 2072
 
3.4%
B 2048
 
3.4%
4 1975
 
3.3%
5 1897
 
3.1%
Other values (27) 30597
50.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 31217
51.5%
Decimal Number 21265
35.1%
Space Separator 8124
 
13.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 2385
 
7.6%
N 2142
 
6.9%
S 2072
 
6.6%
B 2048
 
6.6%
A 1872
 
6.0%
E 1863
 
6.0%
D 1803
 
5.8%
P 1738
 
5.6%
H 1686
 
5.4%
T 1606
 
5.1%
Other values (16) 12002
38.4%
Decimal Number
ValueCountFrequency (%)
1 4089
19.2%
2 2848
13.4%
3 2429
11.4%
4 1975
9.3%
5 1897
8.9%
6 1856
8.7%
7 1651
7.8%
8 1572
 
7.4%
9 1521
 
7.2%
0 1427
 
6.7%
Space Separator
ValueCountFrequency (%)
8124
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 31217
51.5%
Common 29389
48.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 2385
 
7.6%
N 2142
 
6.9%
S 2072
 
6.6%
B 2048
 
6.6%
A 1872
 
6.0%
E 1863
 
6.0%
D 1803
 
5.8%
P 1738
 
5.6%
H 1686
 
5.4%
T 1606
 
5.1%
Other values (16) 12002
38.4%
Common
ValueCountFrequency (%)
8124
27.6%
1 4089
13.9%
2 2848
 
9.7%
3 2429
 
8.3%
4 1975
 
6.7%
5 1897
 
6.5%
6 1856
 
6.3%
7 1651
 
5.6%
8 1572
 
5.3%
9 1521
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60606
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8124
 
13.4%
1 4089
 
6.7%
2 2848
 
4.7%
3 2429
 
4.0%
L 2385
 
3.9%
N 2142
 
3.5%
S 2072
 
3.4%
B 2048
 
3.4%
4 1975
 
3.3%
5 1897
 
3.1%
Other values (27) 30597
50.5%

post_area
Categorical

Distinct1937
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
PR5
 
28
TQ12
 
22
WA4
 
22
PR4
 
21
PR8
 
21
Other values (1932)
8010 

Length

Max length4
Median length3
Mean length3.4601182
Min length2

Characters and Unicode

Total characters28110
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique449 ?
Unique (%)5.5%

Sample

1st rowM51
2nd rowL40
3rd rowTA19
4th rowFK2
5th rowLS23

Common Values

ValueCountFrequency (%)
PR5 28
 
0.3%
TQ12 22
 
0.3%
WA4 22
 
0.3%
PR4 21
 
0.3%
PR8 21
 
0.3%
LE7 21
 
0.3%
TS5 20
 
0.2%
M12 20
 
0.2%
HD7 19
 
0.2%
M13 19
 
0.2%
Other values (1927) 7911
97.4%

Length

2023-06-05T22:32:23.924013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pr5 28
 
0.3%
wa4 22
 
0.3%
tq12 22
 
0.3%
pr4 21
 
0.3%
pr8 21
 
0.3%
le7 21
 
0.3%
ts5 20
 
0.2%
m12 20
 
0.2%
hd7 19
 
0.2%
m13 19
 
0.2%
Other values (1927) 7911
97.4%

Most occurring characters

ValueCountFrequency (%)
1 3355
 
11.9%
2 2011
 
7.2%
3 1571
 
5.6%
L 1348
 
4.8%
4 1243
 
4.4%
S 1241
 
4.4%
N 1094
 
3.9%
B 1066
 
3.8%
6 1064
 
3.8%
5 1063
 
3.8%
Other values (26) 13054
46.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 14969
53.3%
Decimal Number 13141
46.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 1348
 
9.0%
S 1241
 
8.3%
N 1094
 
7.3%
B 1066
 
7.1%
A 882
 
5.9%
E 819
 
5.5%
T 816
 
5.5%
P 761
 
5.1%
D 736
 
4.9%
C 716
 
4.8%
Other values (16) 5490
36.7%
Decimal Number
ValueCountFrequency (%)
1 3355
25.5%
2 2011
15.3%
3 1571
12.0%
4 1243
 
9.5%
6 1064
 
8.1%
5 1063
 
8.1%
7 835
 
6.4%
8 699
 
5.3%
0 667
 
5.1%
9 633
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 14969
53.3%
Common 13141
46.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 1348
 
9.0%
S 1241
 
8.3%
N 1094
 
7.3%
B 1066
 
7.1%
A 882
 
5.9%
E 819
 
5.5%
T 816
 
5.5%
P 761
 
5.1%
D 736
 
4.9%
C 716
 
4.8%
Other values (16) 5490
36.7%
Common
ValueCountFrequency (%)
1 3355
25.5%
2 2011
15.3%
3 1571
12.0%
4 1243
 
9.5%
6 1064
 
8.1%
5 1063
 
8.1%
7 835
 
6.4%
8 699
 
5.3%
0 667
 
5.1%
9 633
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3355
 
11.9%
2 2011
 
7.2%
3 1571
 
5.6%
L 1348
 
4.8%
4 1243
 
4.4%
S 1241
 
4.4%
N 1094
 
3.9%
B 1066
 
3.8%
6 1064
 
3.8%
5 1063
 
3.8%
Other values (26) 13054
46.4%

Average_Credit_Card_Transaction
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1209
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.251094
Minimum0
Maximum662.26
Zeros4989
Zeros (%)61.4%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2023-06-05T22:32:24.033759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q323.48
95-th percentile129.925
Maximum662.26
Range662.26
Interquartile range (IQR)23.48

Descriptive statistics

Standard deviation51.147496
Coefficient of variation (CV)2.1997888
Kurtosis20.295455
Mean23.251094
Median Absolute Deviation (MAD)0
Skewness3.7611524
Sum188891.89
Variance2616.0664
MonotonicityNot monotonic
2023-06-05T22:32:24.142438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4989
61.4%
19.99 112
 
1.4%
9.99 86
 
1.1%
11.99 54
 
0.7%
24.99 48
 
0.6%
19.49 46
 
0.6%
4.99 43
 
0.5%
14.99 43
 
0.5%
9.49 40
 
0.5%
15.99 39
 
0.5%
Other values (1199) 2624
32.3%
ValueCountFrequency (%)
0 4989
61.4%
0.01 31
 
0.4%
0.02 8
 
0.1%
0.03 1
 
< 0.1%
0.04 1
 
< 0.1%
0.51 10
 
0.1%
0.52 4
 
< 0.1%
0.54 1
 
< 0.1%
1.03 1
 
< 0.1%
1.04 1
 
< 0.1%
ValueCountFrequency (%)
662.26 1
< 0.1%
592.36 1
< 0.1%
571.74 1
< 0.1%
565.36 1
< 0.1%
481.36 1
< 0.1%
477.82 1
< 0.1%
467.88 1
< 0.1%
461.93 1
< 0.1%
436.8 1
< 0.1%
421.76 1
< 0.1%

Balance_Transfer
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1860
Distinct (%)22.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.082922
Minimum0
Maximum2951.76
Zeros3524
Zeros (%)43.4%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2023-06-05T22:32:24.260191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median17.485
Q364.99
95-th percentile186.9005
Maximum2951.76
Range2951.76
Interquartile range (IQR)64.99

Descriptive statistics

Standard deviation79.084692
Coefficient of variation (CV)1.7161388
Kurtosis231.64235
Mean46.082922
Median Absolute Deviation (MAD)17.485
Skewness8.1737344
Sum374377.66
Variance6254.3886
MonotonicityNot monotonic
2023-06-05T22:32:24.370008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3524
43.4%
0.01 155
 
1.9%
24.99 135
 
1.7%
29.99 114
 
1.4%
19.99 72
 
0.9%
34.99 63
 
0.8%
25.99 52
 
0.6%
0.51 44
 
0.5%
44.99 43
 
0.5%
0.02 41
 
0.5%
Other values (1850) 3881
47.8%
ValueCountFrequency (%)
0 3524
43.4%
0.01 155
 
1.9%
0.02 41
 
0.5%
0.03 12
 
0.1%
0.04 2
 
< 0.1%
0.05 5
 
0.1%
0.46 1
 
< 0.1%
0.51 44
 
0.5%
0.52 35
 
0.4%
0.53 9
 
0.1%
ValueCountFrequency (%)
2951.76 1
< 0.1%
860.83 1
< 0.1%
749.38 1
< 0.1%
659.21 1
< 0.1%
644.87 1
< 0.1%
601.85 1
< 0.1%
596.85 1
< 0.1%
583.87 1
< 0.1%
573.4 1
< 0.1%
570.86 1
< 0.1%

Term_Deposit
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1215
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.284649
Minimum0
Maximum784.82
Zeros4587
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2023-06-05T22:32:24.488857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q334.49
95-th percentile124.957
Maximum784.82
Range784.82
Interquartile range (IQR)34.49

Descriptive statistics

Standard deviation54.133537
Coefficient of variation (CV)1.9840291
Kurtosis28.68558
Mean27.284649
Median Absolute Deviation (MAD)0
Skewness4.1741626
Sum221660.49
Variance2930.4398
MonotonicityNot monotonic
2023-06-05T22:32:24.604057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4587
56.5%
24.99 128
 
1.6%
29.99 125
 
1.5%
19.99 87
 
1.1%
14.99 75
 
0.9%
9.99 64
 
0.8%
34.99 62
 
0.8%
0.01 58
 
0.7%
29.49 47
 
0.6%
24.49 40
 
0.5%
Other values (1205) 2851
35.1%
ValueCountFrequency (%)
0 4587
56.5%
0.01 58
 
0.7%
0.02 12
 
0.1%
0.03 1
 
< 0.1%
0.51 28
 
0.3%
0.52 10
 
0.1%
0.53 1
 
< 0.1%
1.02 1
 
< 0.1%
1.03 3
 
< 0.1%
1.6 1
 
< 0.1%
ValueCountFrequency (%)
784.82 1
< 0.1%
738.67 1
< 0.1%
716.12 1
< 0.1%
597.76 1
< 0.1%
539.18 1
< 0.1%
522.77 1
< 0.1%
514.26 1
< 0.1%
505.54 1
< 0.1%
493.78 1
< 0.1%
484.73 1
< 0.1%

Life_Insurance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2655
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.317939
Minimum0
Maximum2930.41
Zeros2454
Zeros (%)30.2%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2023-06-05T22:32:24.725597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median31.475
Q392.8875
95-th percentile239.874
Maximum2930.41
Range2930.41
Interquartile range (IQR)92.8875

Descriptive statistics

Standard deviation95.762451
Coefficient of variation (CV)1.4660972
Kurtosis106.59993
Mean65.317939
Median Absolute Deviation (MAD)31.475
Skewness5.5093788
Sum530642.94
Variance9170.4471
MonotonicityNot monotonic
2023-06-05T22:32:24.833764image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2454
30.2%
0.01 77
 
0.9%
27.99 58
 
0.7%
29.99 52
 
0.6%
22.99 47
 
0.6%
34.99 44
 
0.5%
25.99 44
 
0.5%
24.99 40
 
0.5%
17.99 38
 
0.5%
19.99 38
 
0.5%
Other values (2645) 5232
64.4%
ValueCountFrequency (%)
0 2454
30.2%
0.01 77
 
0.9%
0.02 25
 
0.3%
0.03 7
 
0.1%
0.08 1
 
< 0.1%
0.44 1
 
< 0.1%
0.51 28
 
0.3%
0.52 15
 
0.2%
0.53 6
 
0.1%
0.54 3
 
< 0.1%
ValueCountFrequency (%)
2930.41 1
< 0.1%
1005.53 1
< 0.1%
817.63 1
< 0.1%
799.12 1
< 0.1%
795.79 1
< 0.1%
774.6 1
< 0.1%
748.46 1
< 0.1%
734.1 1
< 0.1%
726.67 1
< 0.1%
719.6 1
< 0.1%

Medical_Insurance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1362
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.826195
Minimum0
Maximum591.04
Zeros4046
Zeros (%)49.8%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2023-06-05T22:32:24.949731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.01
Q326.97
95-th percentile81.4155
Maximum591.04
Range591.04
Interquartile range (IQR)26.97

Descriptive statistics

Standard deviation32.022332
Coefficient of variation (CV)1.7009455
Kurtosis22.795401
Mean18.826195
Median Absolute Deviation (MAD)0.01
Skewness3.3619402
Sum152944.01
Variance1025.4297
MonotonicityNot monotonic
2023-06-05T22:32:25.316283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4046
49.8%
9.99 168
 
2.1%
9.49 61
 
0.8%
29.99 56
 
0.7%
10.99 54
 
0.7%
7.49 54
 
0.7%
6.99 51
 
0.6%
4.99 51
 
0.6%
19.99 48
 
0.6%
19.98 44
 
0.5%
Other values (1352) 3491
43.0%
ValueCountFrequency (%)
0 4046
49.8%
0.01 30
 
0.4%
0.02 4
 
< 0.1%
0.48 1
 
< 0.1%
0.51 8
 
0.1%
0.52 4
 
< 0.1%
1 2
 
< 0.1%
1.49 1
 
< 0.1%
1.54 1
 
< 0.1%
1.98 1
 
< 0.1%
ValueCountFrequency (%)
591.04 1
< 0.1%
350.71 1
< 0.1%
306.85 1
< 0.1%
265.84 1
< 0.1%
244.3 1
< 0.1%
241.79 1
< 0.1%
235.36 1
< 0.1%
233.76 1
< 0.1%
233.38 1
< 0.1%
231.73 1
< 0.1%

Average_A/C_Balance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1923
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.838023
Minimum0
Maximum626.24
Zeros2806
Zeros (%)34.5%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2023-06-05T22:32:25.434107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median14.98
Q345.9225
95-th percentile120.692
Maximum626.24
Range626.24
Interquartile range (IQR)45.9225

Descriptive statistics

Standard deviation45.24944
Coefficient of variation (CV)1.421239
Kurtosis12.495594
Mean31.838023
Median Absolute Deviation (MAD)14.98
Skewness2.6899852
Sum258652.1
Variance2047.5118
MonotonicityNot monotonic
2023-06-05T22:32:25.542322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2806
34.5%
29.99 112
 
1.4%
4.99 111
 
1.4%
11.99 96
 
1.2%
9.99 84
 
1.0%
14.99 74
 
0.9%
24.99 69
 
0.8%
2.99 49
 
0.6%
34.99 48
 
0.6%
14.49 41
 
0.5%
Other values (1913) 4634
57.0%
ValueCountFrequency (%)
0 2806
34.5%
0.01 27
 
0.3%
0.02 4
 
< 0.1%
0.05 1
 
< 0.1%
0.47 1
 
< 0.1%
0.51 9
 
0.1%
0.52 2
 
< 0.1%
0.97 1
 
< 0.1%
0.98 1
 
< 0.1%
1 1
 
< 0.1%
ValueCountFrequency (%)
626.24 1
< 0.1%
415.14 1
< 0.1%
410.71 1
< 0.1%
402.6 1
< 0.1%
398.06 1
< 0.1%
380.75 1
< 0.1%
367.34 1
< 0.1%
351.38 1
< 0.1%
348.18 1
< 0.1%
347.79 1
< 0.1%

Personal_Loan
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1477
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.41583
Minimum0
Maximum4905.93
Zeros5134
Zeros (%)63.2%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2023-06-05T22:32:25.656387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q320.8275
95-th percentile132.8025
Maximum4905.93
Range4905.93
Interquartile range (IQR)20.8275

Descriptive statistics

Standard deviation85.13015
Coefficient of variation (CV)3.3494933
Kurtosis1354.453
Mean25.41583
Median Absolute Deviation (MAD)0
Skewness26.159596
Sum206478.2
Variance7247.1424
MonotonicityNot monotonic
2023-06-05T22:32:25.773813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5134
63.2%
15.99 47
 
0.6%
6.99 35
 
0.4%
14.99 30
 
0.4%
5.99 29
 
0.4%
8.99 27
 
0.3%
0.01 26
 
0.3%
13.99 26
 
0.3%
11.99 25
 
0.3%
17.99 23
 
0.3%
Other values (1467) 2722
33.5%
ValueCountFrequency (%)
0 5134
63.2%
0.01 26
 
0.3%
0.02 7
 
0.1%
0.51 6
 
0.1%
0.52 4
 
< 0.1%
0.53 2
 
< 0.1%
0.55 1
 
< 0.1%
0.99 2
 
< 0.1%
1 3
 
< 0.1%
1.02 1
 
< 0.1%
ValueCountFrequency (%)
4905.93 1
< 0.1%
1309.08 1
< 0.1%
1280.2 1
< 0.1%
1173.96 1
< 0.1%
898.39 1
< 0.1%
801.76 1
< 0.1%
772.02 1
< 0.1%
719.65 1
< 0.1%
704.34 1
< 0.1%
661.91 1
< 0.1%

Investment_in_Mutual_Fund
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2130
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.723628
Minimum0
Maximum2561.27
Zeros2602
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2023-06-05T22:32:25.883275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23.48
Q359.44
95-th percentile148.254
Maximum2561.27
Range2561.27
Interquartile range (IQR)59.44

Descriptive statistics

Standard deviation64.416023
Coefficient of variation (CV)1.543874
Kurtosis299.09113
Mean41.723628
Median Absolute Deviation (MAD)23.48
Skewness9.6672471
Sum338962.75
Variance4149.4241
MonotonicityNot monotonic
2023-06-05T22:32:26.000486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2602
32.0%
11.99 111
 
1.4%
9.99 81
 
1.0%
13.99 70
 
0.9%
23.98 64
 
0.8%
23.48 49
 
0.6%
11.49 47
 
0.6%
19.98 47
 
0.6%
17.99 39
 
0.5%
0.01 39
 
0.5%
Other values (2120) 4975
61.2%
ValueCountFrequency (%)
0 2602
32.0%
0.01 39
 
0.5%
0.02 15
 
0.2%
0.03 3
 
< 0.1%
0.06 1
 
< 0.1%
0.16 1
 
< 0.1%
0.48 1
 
< 0.1%
0.51 20
 
0.2%
0.52 11
 
0.1%
0.53 1
 
< 0.1%
ValueCountFrequency (%)
2561.27 1
< 0.1%
765.03 1
< 0.1%
648.54 1
< 0.1%
646.39 1
< 0.1%
633.89 1
< 0.1%
587.61 1
< 0.1%
576.61 1
< 0.1%
565.99 1
< 0.1%
545 1
< 0.1%
522.58 1
< 0.1%
Distinct718
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0572464
Minimum0
Maximum156.87
Zeros5133
Zeros (%)63.2%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2023-06-05T22:32:26.115218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35.49
95-th percentile32.467
Maximum156.87
Range156.87
Interquartile range (IQR)5.49

Descriptive statistics

Standard deviation12.673374
Coefficient of variation (CV)2.0922665
Kurtosis14.674817
Mean6.0572464
Median Absolute Deviation (MAD)0
Skewness3.1917131
Sum49209.07
Variance160.6144
MonotonicityNot monotonic
2023-06-05T22:32:26.228879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5133
63.2%
1 218
 
2.7%
2 107
 
1.3%
4.99 98
 
1.2%
9.99 80
 
1.0%
19.99 69
 
0.8%
4.49 62
 
0.8%
2.49 59
 
0.7%
2.99 48
 
0.6%
3 46
 
0.6%
Other values (708) 2204
27.1%
ValueCountFrequency (%)
0 5133
63.2%
0.01 2
 
< 0.1%
0.1 2
 
< 0.1%
0.2 4
 
< 0.1%
0.45 2
 
< 0.1%
0.5 6
 
0.1%
0.65 2
 
< 0.1%
0.74 1
 
< 0.1%
0.85 1
 
< 0.1%
0.97 1
 
< 0.1%
ValueCountFrequency (%)
156.87 1
< 0.1%
138.56 1
< 0.1%
124.76 1
< 0.1%
121.44 1
< 0.1%
119.87 1
< 0.1%
102.46 1
< 0.1%
101.94 1
< 0.1%
101.81 1
< 0.1%
96.44 1
< 0.1%
95.92 1
< 0.1%

Home_Loan
Real number (ℝ)

Distinct760
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4169141
Minimum0
Maximum162.35
Zeros5609
Zeros (%)69.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2023-06-05T22:32:26.351957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34.49
95-th percentile23.9785
Maximum162.35
Range162.35
Interquartile range (IQR)4.49

Descriptive statistics

Standard deviation9.9457466
Coefficient of variation (CV)2.251741
Kurtosis26.081721
Mean4.4169141
Median Absolute Deviation (MAD)0
Skewness3.977657
Sum35883.01
Variance98.917876
MonotonicityNot monotonic
2023-06-05T22:32:26.465332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5609
69.0%
4.99 91
 
1.1%
9.99 77
 
0.9%
4.49 73
 
0.9%
3.99 54
 
0.7%
3.49 46
 
0.6%
2.99 39
 
0.5%
1.99 37
 
0.5%
7.99 36
 
0.4%
14.99 36
 
0.4%
Other values (750) 2026
 
24.9%
ValueCountFrequency (%)
0 5609
69.0%
0.01 8
 
0.1%
0.5 3
 
< 0.1%
0.51 1
 
< 0.1%
0.74 2
 
< 0.1%
0.99 6
 
0.1%
1 7
 
0.1%
1.19 1
 
< 0.1%
1.24 7
 
0.1%
1.49 18
 
0.2%
ValueCountFrequency (%)
162.35 1
< 0.1%
121.92 1
< 0.1%
114.39 1
< 0.1%
110.17 1
< 0.1%
101.02 1
< 0.1%
91.26 1
< 0.1%
90.49 1
< 0.1%
86.83 1
< 0.1%
86.37 1
< 0.1%
82.86 1
< 0.1%

Online_Purchase_Amount
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1128
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.198658
Minimum0
Maximum4306.42
Zeros5700
Zeros (%)70.2%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2023-06-05T22:32:26.590185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37.48
95-th percentile95.4685
Maximum4306.42
Range4306.42
Interquartile range (IQR)7.48

Descriptive statistics

Standard deviation92.343126
Coefficient of variation (CV)4.8098739
Kurtosis769.59452
Mean19.198658
Median Absolute Deviation (MAD)0
Skewness21.763954
Sum155969.9
Variance8527.253
MonotonicityNot monotonic
2023-06-05T22:32:26.697332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5700
70.2%
3.99 50
 
0.6%
11.99 47
 
0.6%
7.99 35
 
0.4%
4.49 32
 
0.4%
4.99 29
 
0.4%
14.99 29
 
0.4%
9.99 27
 
0.3%
2.99 26
 
0.3%
19.99 24
 
0.3%
Other values (1118) 2125
 
26.2%
ValueCountFrequency (%)
0 5700
70.2%
0.01 15
 
0.2%
0.02 4
 
< 0.1%
0.05 1
 
< 0.1%
0.5 2
 
< 0.1%
0.51 7
 
0.1%
0.52 4
 
< 0.1%
0.53 2
 
< 0.1%
0.8 2
 
< 0.1%
0.99 5
 
0.1%
ValueCountFrequency (%)
4306.42 1
< 0.1%
2808.8 1
< 0.1%
2142.62 1
< 0.1%
2033.85 1
< 0.1%
1652.45 1
< 0.1%
1513.15 1
< 0.1%
1071.22 1
< 0.1%
998.99 1
< 0.1%
964.77 1
< 0.1%
956.96 1
< 0.1%

gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
Female
6106 
Male
1987 
Unknown
 
31

Length

Max length7
Median length6
Mean length5.514648
Min length4

Characters and Unicode

Total characters44801
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 6106
75.2%
Male 1987
 
24.5%
Unknown 31
 
0.4%

Length

2023-06-05T22:32:26.795426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T22:32:26.896616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
female 6106
75.2%
male 1987
 
24.5%
unknown 31
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e 14199
31.7%
a 8093
18.1%
l 8093
18.1%
F 6106
13.6%
m 6106
13.6%
M 1987
 
4.4%
n 93
 
0.2%
U 31
 
0.1%
k 31
 
0.1%
o 31
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 36677
81.9%
Uppercase Letter 8124
 
18.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 14199
38.7%
a 8093
22.1%
l 8093
22.1%
m 6106
16.6%
n 93
 
0.3%
k 31
 
0.1%
o 31
 
0.1%
w 31
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
F 6106
75.2%
M 1987
 
24.5%
U 31
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 44801
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 14199
31.7%
a 8093
18.1%
l 8093
18.1%
F 6106
13.6%
m 6106
13.6%
M 1987
 
4.4%
n 93
 
0.2%
U 31
 
0.1%
k 31
 
0.1%
o 31
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44801
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 14199
31.7%
a 8093
18.1%
l 8093
18.1%
F 6106
13.6%
m 6106
13.6%
M 1987
 
4.4%
n 93
 
0.2%
U 31
 
0.1%
k 31
 
0.1%
o 31
 
0.1%

region
Categorical

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
South East
1680 
North West
1517 
Unknown
866 
South West
769 
West Midlands
658 
Other values (8)
2634 

Length

Max length16
Median length15
Mean length9.5967504
Min length5

Characters and Unicode

Total characters77964
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth West
2nd rowNorth West
3rd rowSouth West
4th rowScotland
5th rowUnknown

Common Values

ValueCountFrequency (%)
South East 1680
20.7%
North West 1517
18.7%
Unknown 866
10.7%
South West 769
9.5%
West Midlands 658
 
8.1%
East Midlands 623
 
7.7%
Scotland 615
 
7.6%
North 460
 
5.7%
Wales 437
 
5.4%
East Anglia 344
 
4.2%
Other values (3) 155
 
1.9%

Length

2023-06-05T22:32:26.979976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
west 2944
21.2%
east 2647
19.1%
south 2449
17.6%
north 1977
14.2%
midlands 1281
9.2%
unknown 866
 
6.2%
scotland 615
 
4.4%
wales 437
 
3.1%
anglia 344
 
2.5%
northern 135
 
1.0%
Other values (6) 190
 
1.4%

Most occurring characters

ValueCountFrequency (%)
t 10767
13.8%
s 7334
 
9.4%
o 6057
 
7.8%
5761
 
7.4%
a 5484
 
7.0%
n 5138
 
6.6%
h 4566
 
5.9%
e 3671
 
4.7%
W 3381
 
4.3%
d 3317
 
4.3%
Other values (17) 22488
28.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 58333
74.8%
Uppercase Letter 13870
 
17.8%
Space Separator 5761
 
7.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 10767
18.5%
s 7334
12.6%
o 6057
10.4%
a 5484
9.4%
n 5138
8.8%
h 4566
7.8%
e 3671
 
6.3%
d 3317
 
5.7%
l 2837
 
4.9%
u 2449
 
4.2%
Other values (7) 6713
11.5%
Uppercase Letter
ValueCountFrequency (%)
W 3381
24.4%
S 3064
22.1%
E 2647
19.1%
N 2112
15.2%
M 1296
 
9.3%
U 866
 
6.2%
A 344
 
2.5%
I 155
 
1.1%
C 5
 
< 0.1%
Space Separator
ValueCountFrequency (%)
5761
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 72203
92.6%
Common 5761
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 10767
14.9%
s 7334
 
10.2%
o 6057
 
8.4%
a 5484
 
7.6%
n 5138
 
7.1%
h 4566
 
6.3%
e 3671
 
5.1%
W 3381
 
4.7%
d 3317
 
4.6%
S 3064
 
4.2%
Other values (16) 19424
26.9%
Common
ValueCountFrequency (%)
5761
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 10767
13.8%
s 7334
 
9.4%
o 6057
 
7.8%
5761
 
7.4%
a 5484
 
7.0%
n 5138
 
6.6%
h 4566
 
5.9%
e 3671
 
4.7%
W 3381
 
4.3%
d 3317
 
4.3%
Other values (17) 22488
28.8%

Investment_in_Commudity
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3081
Distinct (%)37.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.152749
Minimum0
Maximum1231.09
Zeros825
Zeros (%)10.2%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2023-06-05T22:32:27.079391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18.2825
median23.59
Q349.795
95-th percentile114.642
Maximum1231.09
Range1231.09
Interquartile range (IQR)41.5125

Descriptive statistics

Standard deviation42.474953
Coefficient of variation (CV)1.1748748
Kurtosis84.358997
Mean36.152749
Median Absolute Deviation (MAD)18.19
Skewness4.8161222
Sum293704.93
Variance1804.1217
MonotonicityNot monotonic
2023-06-05T22:32:27.194609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 825
 
10.2%
4 49
 
0.6%
5 44
 
0.5%
6 43
 
0.5%
2 37
 
0.5%
7 37
 
0.5%
3 29
 
0.4%
3.6 29
 
0.4%
3.9 27
 
0.3%
3.2 25
 
0.3%
Other values (3071) 6979
85.9%
ValueCountFrequency (%)
0 825
10.2%
0.01 5
 
0.1%
0.1 20
 
0.2%
0.12 1
 
< 0.1%
0.3 1
 
< 0.1%
0.4 2
 
< 0.1%
0.41 1
 
< 0.1%
0.5 1
 
< 0.1%
0.6 1
 
< 0.1%
1 8
 
0.1%
ValueCountFrequency (%)
1231.09 1
< 0.1%
412.96 1
< 0.1%
385.24 1
< 0.1%
384.17 1
< 0.1%
373.3 1
< 0.1%
370.39 1
< 0.1%
342.43 1
< 0.1%
331.73 1
< 0.1%
318.85 1
< 0.1%
318.78 1
< 0.1%

Investment_in_Equity
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2812
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.442475
Minimum0
Maximum1279.1
Zeros915
Zeros (%)11.3%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2023-06-05T22:32:27.307404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.66
median12.82
Q327.9725
95-th percentile68.4585
Maximum1279.1
Range1279.1
Interquartile range (IQR)23.3125

Descriptive statistics

Standard deviation32.26166
Coefficient of variation (CV)1.5045679
Kurtosis334.80082
Mean21.442475
Median Absolute Deviation (MAD)9.91
Skewness11.524906
Sum174198.67
Variance1040.8147
MonotonicityNot monotonic
2023-06-05T22:32:27.425019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 915
 
11.3%
3.33 55
 
0.7%
2 47
 
0.6%
5 42
 
0.5%
1.67 38
 
0.5%
2.5 35
 
0.4%
4.17 29
 
0.4%
6.66 28
 
0.3%
4 28
 
0.3%
2.33 27
 
0.3%
Other values (2802) 6880
84.7%
ValueCountFrequency (%)
0 915
11.3%
0.02 1
 
< 0.1%
0.08 1
 
< 0.1%
0.09 8
 
0.1%
0.17 18
 
0.2%
0.18 1
 
< 0.1%
0.21 1
 
< 0.1%
0.25 2
 
< 0.1%
0.29 1
 
< 0.1%
0.33 9
 
0.1%
ValueCountFrequency (%)
1279.1 1
< 0.1%
717.74 1
< 0.1%
556.19 1
< 0.1%
419.99 1
< 0.1%
408.64 1
< 0.1%
368.18 1
< 0.1%
345.89 1
< 0.1%
338.76 1
< 0.1%
316.24 1
< 0.1%
292.4 1
< 0.1%

Investment_in_Derivative
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3269
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.530652
Minimum0
Maximum1771.16
Zeros445
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2023-06-05T22:32:27.542427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18.74
median21.14
Q342.3925
95-th percentile96.0185
Maximum1771.16
Range1771.16
Interquartile range (IQR)33.6525

Descriptive statistics

Standard deviation39.48066
Coefficient of variation (CV)1.2521359
Kurtosis472.36738
Mean31.530652
Median Absolute Deviation (MAD)14.89
Skewness12.462312
Sum256155.02
Variance1558.7225
MonotonicityNot monotonic
2023-06-05T22:32:27.651881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 445
 
5.5%
3.33 40
 
0.5%
5 39
 
0.5%
2 29
 
0.4%
1.67 29
 
0.4%
5.83 26
 
0.3%
4.91 25
 
0.3%
2.5 25
 
0.3%
4.66 23
 
0.3%
6.58 23
 
0.3%
Other values (3259) 7420
91.3%
ValueCountFrequency (%)
0 445
5.5%
0.01 1
 
< 0.1%
0.09 10
 
0.1%
0.1 1
 
< 0.1%
0.17 8
 
0.1%
0.25 1
 
< 0.1%
0.33 6
 
0.1%
0.42 3
 
< 0.1%
0.5 7
 
0.1%
0.58 2
 
< 0.1%
ValueCountFrequency (%)
1771.16 1
< 0.1%
456.12 1
< 0.1%
421.55 1
< 0.1%
411.39 1
< 0.1%
389.41 1
< 0.1%
330.28 1
< 0.1%
319.8 1
< 0.1%
286.96 1
< 0.1%
285.76 1
< 0.1%
276.16 1
< 0.1%

Portfolio_Balance
Real number (ℝ)

Distinct6884
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.353368
Minimum-78.43
Maximum4283.56
Zeros0
Zeros (%)0.0%
Negative852
Negative (%)10.5%
Memory size63.6 KiB
2023-06-05T22:32:27.772748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-78.43
5-th percentile-13.54
Q126.2775
median65.56
Q3123.97
95-th percentile271.5595
Maximum4283.56
Range4361.99
Interquartile range (IQR)97.6925

Descriptive statistics

Standard deviation108.30354
Coefficient of variation (CV)1.2120812
Kurtosis283.27618
Mean89.353368
Median Absolute Deviation (MAD)45.695
Skewness8.8954715
Sum725906.76
Variance11729.656
MonotonicityNot monotonic
2023-06-05T22:32:27.879016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.45 5
 
0.1%
57.9 5
 
0.1%
8.89 4
 
< 0.1%
23.38 4
 
< 0.1%
4.09 4
 
< 0.1%
51.05 4
 
< 0.1%
89.12 4
 
< 0.1%
118.52 4
 
< 0.1%
30 4
 
< 0.1%
102.64 4
 
< 0.1%
Other values (6874) 8082
99.5%
ValueCountFrequency (%)
-78.43 1
< 0.1%
-77.23 1
< 0.1%
-76.35 1
< 0.1%
-73.35 1
< 0.1%
-72.74 1
< 0.1%
-69.38 1
< 0.1%
-67.42 1
< 0.1%
-66.27 1
< 0.1%
-64.3 1
< 0.1%
-64.16 1
< 0.1%
ValueCountFrequency (%)
4283.56 1
< 0.1%
1097.44 1
< 0.1%
1053.8 1
< 0.1%
1024.68 1
< 0.1%
952.49 1
< 0.1%
862.32 1
< 0.1%
844.24 1
< 0.1%
790.83 1
< 0.1%
769.02 1
< 0.1%
763.22 1
< 0.1%

Revenue_Grid
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
2
7264 
1
860 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8124
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 7264
89.4%
1 860
 
10.6%

Length

2023-06-05T22:32:27.969574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T22:32:28.058318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2 7264
89.4%
1 860
 
10.6%

Most occurring characters

ValueCountFrequency (%)
2 7264
89.4%
1 860
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8124
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 7264
89.4%
1 860
 
10.6%

Most occurring scripts

ValueCountFrequency (%)
Common 8124
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 7264
89.4%
1 860
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8124
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 7264
89.4%
1 860
 
10.6%

Interactions

2023-06-05T22:32:19.610790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:31:53.099582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:31:54.760343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:31:56.413617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:31:58.114861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:31:59.698790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:01.459852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:03.053573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-06-05T22:32:00.987346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:02.598842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:04.185828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:05.915765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:07.486938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:09.050123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:10.861043image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:12.532826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:14.116312image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:15.711274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:17.523183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:19.144919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:20.785801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:31:54.319684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:31:56.031504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:31:57.740778image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:31:59.331123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:01.077728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:02.685376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:04.271964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:06.003132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:07.571153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:09.134425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:10.953001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:12.623868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:14.198709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:15.798938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:17.609827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:19.233511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:20.875411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:31:54.485491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:31:56.128062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:31:57.836203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:31:59.423612image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:01.174179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:02.777902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:04.364738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:06.096072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:07.662709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:09.226380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:11.051945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:12.721595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:14.289633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:15.891237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:17.702407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:19.329379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:20.965405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:31:54.578411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:31:56.225447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:31:57.929711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:31:59.517588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:01.270119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:02.871468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:04.456659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:06.189954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:07.753936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:09.317683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:11.151134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:12.819436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:14.380347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:15.985263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:17.794252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:19.424639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:21.058434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:31:54.674572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:31:56.323973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:31:58.028159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:31:59.613435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:01.370431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:02.968388image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:04.554236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:06.286786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:07.848554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:09.412342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:11.253508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:12.920815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:14.474731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:16.082771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:17.891270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-06-05T22:32:19.522233image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-06-05T22:32:28.162933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
REF_NOyear_last_movedAverage_Credit_Card_TransactionBalance_TransferTerm_DepositLife_InsuranceMedical_InsuranceAverage_A/C_BalancePersonal_LoanInvestment_in_Mutual_FundInvestment_Tax_Saving_BondHome_LoanOnline_Purchase_AmountInvestment_in_CommudityInvestment_in_EquityInvestment_in_DerivativePortfolio_Balancechildrenage_bandstatusoccupationoccupation_partnerhome_statusfamily_incomeself_employedself_employed_partnerTVareagenderregionRevenue_Grid
REF_NO1.0000.009-0.001-0.0130.0030.0150.016-0.0100.0100.0040.010-0.0160.0180.0120.0010.0140.0090.0100.0150.0210.0000.0120.0160.0140.0320.0000.0000.0210.0130.017
year_last_moved0.0091.0000.0250.010-0.0020.0070.0160.0070.0730.0050.0280.0100.0220.0200.0300.0240.0240.0500.5230.5310.1990.0930.6330.4800.0000.0240.0300.0000.0150.012
Average_Credit_Card_Transaction-0.0010.0251.0000.1560.3820.1650.4500.1820.0940.1480.1690.1270.1920.5140.2110.2670.3450.0000.0150.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.276
Balance_Transfer-0.0130.0100.1561.0000.2250.5430.1560.4110.1340.3510.1750.1870.1950.6840.3980.4990.5320.0000.0300.0030.0000.0000.0200.0000.0000.0000.0000.0490.0210.015
Term_Deposit0.003-0.0020.3820.2251.0000.2870.4510.2490.1680.2250.2160.1540.2340.5820.3080.3840.4400.0000.0060.0000.0000.0000.0000.0170.0000.0000.0000.0090.0070.013
Life_Insurance0.0150.0070.1650.5430.2871.0000.2000.5020.2210.4530.2660.2690.2640.7430.5260.7870.6710.0040.0170.0090.0050.0000.0000.0000.0000.0130.0000.0000.0000.176
Medical_Insurance0.0160.0160.4500.1560.4510.2001.0000.2400.1470.2350.2110.1630.2400.5090.3030.4340.4180.0130.0070.0000.0000.0090.0290.0200.0000.0140.0000.0000.0000.095
Average_A/C_Balance-0.0100.0070.1820.4110.2490.5020.2401.0000.1960.4540.3150.3300.2930.4810.7140.7000.5770.0100.0000.0230.0050.0000.0000.0000.0000.0090.0000.0050.0140.104
Personal_Loan0.0100.0730.0940.1340.1680.2210.1470.1961.0000.2030.2750.1780.2100.2030.5030.4420.3480.0220.0000.0000.0180.0000.0000.0000.0000.0040.0110.0000.0080.000
Investment_in_Mutual_Fund0.0040.0050.1480.3510.2250.4530.2350.4540.2031.0000.2710.2560.2670.4200.7410.7020.5630.0020.0120.0000.0000.0180.0000.0000.0000.0000.0000.0230.0100.019
Investment_Tax_Saving_Bond0.0100.0280.1690.1750.2160.2660.2110.3150.2750.2711.0000.3070.3440.2830.4780.4280.3740.0410.0250.0000.0120.0000.0000.0080.0000.0370.0000.0000.0000.302
Home_Loan-0.0160.0100.1270.1870.1540.2690.1630.3300.1780.2560.3071.0000.2480.2460.3990.3390.3020.0250.0150.0000.0000.0000.0000.0000.0270.0330.0080.0000.0000.000
Online_Purchase_Amount0.0180.0220.1920.1950.2340.2640.2400.2930.2100.2670.3440.2481.0000.3130.5200.3700.3900.0250.0000.0000.0000.0000.0000.0000.0000.0280.0000.0000.0000.215
Investment_in_Commudity0.0120.0200.5140.6840.5820.7430.5090.4810.2030.4200.2830.2460.3131.0000.5150.7470.7700.0000.0320.0000.0180.0000.0120.0130.0000.0000.0000.0000.0120.096
Investment_in_Equity0.0010.0300.2110.3980.3080.5260.3030.7140.5030.7410.4780.3990.5200.5151.0000.8660.7260.0250.0000.0000.0000.0000.0000.0000.0000.0260.0100.0190.0000.129
Investment_in_Derivative0.0140.0240.2670.4990.3840.7870.4340.7000.4420.7020.4280.3390.3700.7470.8661.0000.8290.0000.0260.0000.0000.0000.0000.0000.0000.0000.0210.0020.0110.024
Portfolio_Balance0.0090.0240.3450.5320.4400.6710.4180.5770.3480.5630.3740.3020.3900.7700.7260.8291.0000.0300.0320.0000.0220.0000.0000.0000.0000.0000.0100.0060.0000.083
children0.0100.0500.0000.0000.0000.0040.0130.0100.0220.0020.0410.0250.0250.0000.0250.0000.0301.0000.3120.1170.1910.1980.0280.1380.0630.1150.0270.0440.0240.006
age_band0.0150.5230.0150.0300.0060.0170.0070.0000.0000.0120.0250.0150.0000.0320.0000.0260.0320.3121.0000.3850.2780.2220.3170.2030.0950.1500.0210.0520.0210.000
status0.0210.5310.0000.0030.0000.0090.0000.0230.0000.0000.0000.0000.0000.0000.0000.0000.0000.1170.3851.0000.1770.3340.3490.3040.0170.1260.0280.0600.0320.000
occupation0.0000.1990.0000.0000.0000.0050.0000.0050.0180.0000.0120.0000.0000.0180.0000.0000.0220.1910.2780.1771.0000.2670.1330.2120.3380.1780.0300.2500.0330.000
occupation_partner0.0120.0930.0000.0000.0000.0000.0090.0000.0000.0180.0000.0000.0000.0000.0000.0000.0000.1980.2220.3340.2671.0000.1020.2220.1170.1990.0360.3320.0360.000
home_status0.0160.6330.0000.0200.0000.0000.0290.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0280.3170.3490.1330.1021.0000.2950.0310.0410.0510.0380.0640.000
family_income0.0140.4800.0000.0000.0170.0000.0200.0000.0000.0000.0080.0000.0000.0130.0000.0000.0000.1380.2030.3040.2120.2220.2951.0000.1230.1120.0390.0950.0410.021
self_employed0.0320.0000.0220.0000.0000.0000.0000.0000.0000.0000.0000.0270.0000.0000.0000.0000.0000.0630.0950.0170.3380.1170.0310.1231.0000.2400.0340.1130.0440.000
self_employed_partner0.0000.0240.0000.0000.0000.0130.0140.0090.0040.0000.0370.0330.0280.0000.0260.0000.0000.1150.1500.1260.1780.1990.0410.1120.2401.0000.0090.1100.0230.000
TVarea0.0000.0300.0000.0000.0000.0000.0000.0000.0110.0000.0000.0080.0000.0000.0100.0210.0100.0270.0210.0280.0300.0360.0510.0390.0340.0091.0000.0920.7200.000
gender0.0210.0000.0000.0490.0090.0000.0000.0050.0000.0230.0000.0000.0000.0000.0190.0020.0060.0440.0520.0600.2500.3320.0380.0950.1130.1100.0921.0000.0810.022
region0.0130.0150.0000.0210.0070.0000.0000.0140.0080.0100.0000.0000.0000.0120.0000.0110.0000.0240.0210.0320.0330.0360.0640.0410.0440.0230.7200.0811.0000.005
Revenue_Grid0.0170.0120.2760.0150.0130.1760.0950.1040.0000.0190.3020.0000.2150.0960.1290.0240.0830.0060.0000.0000.0000.0000.0000.0210.0000.0000.0000.0220.0051.000

Missing values

2023-06-05T22:32:21.233850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-05T22:32:21.637492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

REF_NOchildrenage_bandstatusoccupationoccupation_partnerhome_statusfamily_incomeself_employedself_employed_partneryear_last_movedTVareapost_codepost_areaAverage_Credit_Card_TransactionBalance_TransferTerm_DepositLife_InsuranceMedical_InsuranceAverage_A/C_BalancePersonal_LoanInvestment_in_Mutual_FundInvestment_Tax_Saving_BondHome_LoanOnline_Purchase_AmountgenderregionInvestment_in_CommudityInvestment_in_EquityInvestment_in_DerivativePortfolio_BalanceRevenue_Grid
05466231-35PartnerProfessionalProfessionalOwn Home>=35,000NoNo1981MeridianM51 0GUM5126.9829.99312.25299.7988.72108.85175.43134.358.9855.447.68FemaleNorth West151.5581.79136.02360.372
19091Zero45-50PartnerSecretarial/AdminProfessionalOwn Home>=35,000NoNo1997MeridianL40 2AGL4035.9874.480.0099.9610.9948.4515.990.000.000.0018.99FemaleNorth West44.2813.9129.2389.222
29744136-40PartnerManual WorkerManual WorkerRent Privately<22,500, >=20,000YesYes1996HTVTA19 9PTTA190.0024.460.0018.440.000.000.0210.460.000.000.00FemaleSouth West8.581.754.8214.502
310700231-35PartnerManual WorkerManual WorkerOwn Home<25,000, >=22,500NoNo1990Scottish TVFK2 9NGFK244.990.000.000.0029.990.000.000.000.000.000.00FemaleScotland15.000.005.0068.982
41987Zero55-60PartnerHousewifeProfessionalOwn Home>=35,000NoNo1989YorkshireLS23 7DJLS230.000.000.000.000.000.000.009.980.000.000.00FemaleUnknown0.001.661.661.882
53309Zero45-50PartnerSecretarial/AdminBusiness ManagerOwn Home>=35,000NoNo1984UlsterBT17 9NABT179.490.010.000.5155.890.0028.980.000.000.000.00FemaleNorthern Ireland13.184.8314.2333.622
66610Zero36-40PartnerSecretarial/AdminSecretarial/AdminOwn Home<30,000, >=27,500YesNo1986CentralB62 8TFB629.990.000.000.000.0026.9622.9980.421.003.995.49FemaleWest Midlands2.0023.4821.9013.122
710621Zero61-65PartnerRetiredRetiredOwn Home<20,000, >=17,500NoNo1998GranadaPR8 2TYPR80.000.000.000.000.000.000.0029.950.000.000.00MaleNorth West0.004.994.9915.742
82630145-50PartnerProfessionalProfessionalOwn Home>=35,000NoNo1980UnknownCF15 9THCF150.0082.960.0040.4712.490.0028.970.000.000.000.00FemaleUnknown27.184.8313.6636.052
99356336-40PartnerProfessionalHousewifeOwn Home<27,500, >=25,000YesNo1997MeridianM13 9BGM130.000.000.000.000.000.0015.990.0024.470.000.00MaleNorth West0.006.746.748.602
REF_NOchildrenage_bandstatusoccupationoccupation_partnerhome_statusfamily_incomeself_employedself_employed_partneryear_last_movedTVareapost_codepost_areaAverage_Credit_Card_TransactionBalance_TransferTerm_DepositLife_InsuranceMedical_InsuranceAverage_A/C_BalancePersonal_LoanInvestment_in_Mutual_FundInvestment_Tax_Saving_BondHome_LoanOnline_Purchase_AmountgenderregionInvestment_in_CommudityInvestment_in_EquityInvestment_in_DerivativePortfolio_BalanceRevenue_Grid
81149449145-50PartnerRetiredHousewifeOwn Home<12,500, >=10,000NoNo1982YorkshireDN3 3LDDN3148.45104.9827.9999.460.0119.990.000.000.000.000.00MaleUnknown76.183.3319.91116.451
81156341236-40PartnerSecretarial/AdminBusiness ManagerOwn Home<27,500, >=25,000NoNo1986Tyne TeesNE9 7BNNE90.0091.460.00102.440.0028.4660.4688.380.000.0017.48FemaleNorth38.7832.4646.62114.382
81165043Zero61-65PartnerRetiredRetiredOwn Home< 8,000, >= 4,000NoNo1982CarltonSS4 1QASS40.0025.490.000.000.0035.980.000.0011.986.480.00FemaleSouth East5.109.077.99-7.372
8117526126-30PartnerSecretarial/AdminProfessionalOwn Home>=35,000NoNo1998Scottish TVKY12 7YBKY120.0067.9795.4622.9930.9581.450.0049.940.001.490.00FemaleScotland43.4722.1530.8991.372
81187120Zero45-50PartnerProfessionalSecretarial/AdminOwn Home>=35,000NoNo1981GranadaSK9 2ESSK90.0015.9954.98137.440.0088.925.49137.320.0027.9498.92MaleNorth West41.6859.7761.53210.662
81196516336-40PartnerManual WorkerHousewifeOwn Home<20,000, >=17,500NoNo1981MeridianL33 2TWL330.000.000.000.000.000.000.000.000.000.000.00MaleNorth West0.000.000.0015.232
81205897Zero61-65WidowedRetiredUnknownOwn Home< 8,000, >= 4,000NoNo1960CentralDE6 5GYDE60.000.009.490.000.002.990.0046.760.000.000.00FemaleEast Midlands1.908.298.2968.422
81216130141-45Single/Never MarriedHousewifeUnknownRent from Council/HA< 8,000, >= 4,000NoNo1987UlsterBT28 1DXBT280.00107.420.0023.4223.9938.950.00101.371.003.720.00FemaleNorthern Ireland30.9724.1731.46106.062
8122980Zero61-65PartnerRetiredRetiredOwn Home< 4,000NoNo1985MeridianCT10 2JFCT100.0059.480.000.000.000.000.000.000.000.000.00FemaleSouth East11.900.000.00-9.192
81238267341-45PartnerBusiness ManagerHousewifeOwn Home<25,000, >=22,500NoNo1974Tyne TeesTS16 0HGTS1673.9674.4742.98127.4513.4951.4695.9058.4415.474.493.99MaleNorth66.4738.2960.37160.792